Triple
T16624544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haven City |
E403912
|
entity |
| Predicate | inhabitant |
P6481
|
FINISHED |
| Object | Sig |
E717376
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Sig | Statement: [Haven City, inhabitant, Sig]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sig Context triple: [Haven City, inhabitant, Sig]
-
A.
Sig
chosen
Sig is a common shortened form of the given name Sigmund, often used as an informal or familiar nickname.
-
B.
SIG
SIG is the public utility company of Geneva, Switzerland, responsible for providing services such as electricity, gas, water, and energy solutions to the region.
-
C.
SIG
SIG is the IATA airport code for Fernando Luis Ribas Dominicci Airport, a regional airport serving San Juan, Puerto Rico.
-
D.
SIG
SIG is the vehicle registration code for the district of Sigmaringen in the German state of Baden-Württemberg.
-
E.
SIG
SIG is an acronym commonly used by the Association for Computing Machinery to denote its specialized Special Interest Groups that focus on particular areas of computing research and practice.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d883897eb481909eaaa088ba9918d9 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37550ee308190931fd50aeebe1e7e |
completed | April 18, 2026, 12:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007db866e48190886aec7658835543 |
completed | May 10, 2026, 12:44 p.m. |
Created at: April 10, 2026, 5:17 a.m.